Molecular Dynamics Simulations: Advances and Applications

Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer

BACKGROUND

Traditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer.

METHODS

The Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed.

RESULTS

We identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A's interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels.

CONCLUSIONS

This study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.

Cyrene™ as a tyrosinase inhibitor and anti-browning agent

The tyrosinase pathway takes part in the enzymatic process of food browning and is primarily responsible for food spoilage - manifesting itself from a decrease in its nutritional value to a deterioration of taste, which consequently leads to a gradual loss of shelf life. Finding safe and bio-based tyrosinase inhibitors and anti-browning agents may be of great importance in agriculture and food industries. Herein, we showed that Cyrene™ exhibits tyrosinase inhibitory activity (IC50: 268.2 µM), the 1.44 times higher than ascorbic acid (IC50: 386.5 μM). Binding mode studies demonstrated that the carbonyl oxygen of Cyrene™ coordinates with both copper ions. Surprisingly, both hydroxyl groups of Cyrene gem-diol perform a monodentate binding mode with both copper ions, at similar distances. This fact suggests that both compounds could have a similar binding mode and, as consequence, similar biological activities in tyrosinase inhibition assays and anti-browning activities.

Effects ofAs8structure formation on the surface morphology and internal microstructure of GaAs thin films

Gallium arsenide (GaAs) materials have the advantages of high electron mobility, electron saturation drift rate, and other irreplaceable semiconducting properties. They play an important role in the electronics, solar and other fields. However, during GaAs film sedimentary growth, As atoms can undergo segregation to formAs8clusters because of the influence of external factors, which affect the surface morphology and internal structure of these films. In this study, a series of investigations on the deposition and growth of GaAs crystal films were performed. Additionally, the deposition and growth of GaAs thin films were simulated using molecular dynamics. The influence of As8clusters on the surface morphology and internal structure of GaAs films at different incidence angles, velocities and substrate temperatures was studied by using 'defect analysis technology' and 'diamond structure identification' in open source software, along with surface roughness and radial distribution function. Results show that with increasing incident angle, the number ofAs8clusters decreases and film density increases. Increasing incident velocity increases the irregular movement ofAs8clusters in air, and their deposition on the film surface affects the morphology of the film, and the surface roughness increases first and then decreases. Additionally, we investigated the effect of different substrate temperatures on the film surface. Results show that at a substrate temperature of 1173 K, the number ofAs8clusters in the film decreases or the As8clusters disappear, heterogeneous nucleation occurs in the film, and the crystallization rate increases. Although the dislocation line associated with nucleation may affect the mechanical and optical properties of the film, it considerably reduces the annealing effort after the deposition and growth.

A new horizon in the phosphorylated sites of AGA: the structural impact of C163S mutation in aspartylglucosaminuria through molecular dynamics simulation

Aspartylglucosaminuria (AGU) is a lysosomal storage disorder caused by insufficient aspartylglucosaminidase (AGA) activity leading to chronic neurodegeneration. We utilized the PhosphoSitePlus tool to identify the AGA protein's phosphorylation sites. The phosphorylation was induced on the specific residue of the three-dimensional AGA protein, and the structural changes upon phosphorylation were studied via molecular dynamics simulation. Furthermore, the structural behaviour of C163S mutation and C163S mutation with adjacent phosphorylation was investigated. We have examined the structural impact of phosphorylated forms and C163S mutation in AGA. Molecular dynamics simulations (200 ns) exposed patterns of deviation, fluctuation, and change in compactness of Y178 phosphorylated AGA protein (Y178-p), T215 phosphorylated AGA protein (T215-p), T324 phosphorylated AGA protein (T324-p), C163S mutant AGA protein (C163S), and C163S mutation with Y178 phosphorylated AGA protein (C163S-Y178-p). Y178-p, T215-p, and C163S mutation demonstrated an increase in intramolecular hydrogen bonds, leading to greater compactness of the AGA forms. Principle component analysis (PCA) and Gibbs free energy of the phosphorylated/C163S mutation structures exhibit transition in motion/orientation than Wild type (WT). T215-p may be more dominant among these than the other studied phosphorylated forms. It might contribute to hydrolyzing L-asparagine functioning as an asparaginase, thereby regulating neurotransmitter activity. This study revealed structural insights into the phosphorylation of Y178, T215, and T324 in AGA protein. Additionally, it exposed the structural changes of the C163S mutation and C163S-Y178-p of AGA protein. This research will shed light on a better understanding of AGA's phosphorylated mechanism.Communicated by Ramaswamy H. Sarma.

Unravelling the potential of Triflusal as an anti-TB repurposed drug by targeting replication protein DciA

The increasing prevalence of drug-resistant Tuberculosis (TB) is imposing extreme difficulties in controlling the TB infection rate globally, making treatment critically challenging. To combat the prevailing situation, it is crucial to explore new anti-TB drugs with a novel mechanism of action and high efficacy. The Mycobacterium tuberculosis (M.tb)DciA is an essential protein involved in bacterial replication and regulates its growth. DciA interacts with DNA and provides critical help in binding other replication machinery proteins to the DNA. Moreover, the lack of any structural homology of M.tb DciA with human proteins makes it an appropriate target for drug development. In this study, FDA-approved drugs were virtually screened against M.tb DciA to identify potential inhibitors. Four drugs namely Lanreotide, Risedronate, Triflusal, and Zoledronic acid showed higher molecular docking scores. Further, molecular dynamics simulations analysis of DciA-drugs complexes reported stable interaction, more compactness, and reduced atomic motion. The anti-TB activity of drugs was further evaluated under in vitro and ex vivo conditions where Triflusal was observed to have the best possible activity with the MIC of 25 μg/ml. Our findings present novel DciA inhibitors and anti-TB activity of Triflusal. Further investigations on the use of Triflusal may lead to the discovery of a new anti-TB drug.

The Potential of Anti-coronavirus Plant Secondary Metabolites in COVID-19 Drug Discovery as an Alternative to Repurposed Drugs: A Review

In early 2020, a global pandemic was announced due to the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known to cause COVID-19. Despite worldwide efforts, there are only limited options regarding antiviral drug treatments for COVID-19. Although vaccines are now available, issues such as declining efficacy against different SARS-CoV-2 variants and the aging of vaccine-induced immunity highlight the importance of finding more antiviral drugs as a second line of defense against the disease. Drug repurposing has been used to rapidly find COVID-19 therapeutic options. Due to the lack of clinical evidence for the therapeutic benefits and certain serious side effects of repurposed antivirals, the search for an antiviral drug against SARS-CoV-2 with fewer side effects continues. In recent years, numerous studies have included antiviral chemicals from a variety of plant species. A better knowledge of the possible antiviral natural products and their mechanism against SARS-CoV-2 will help to develop stronger and more targeted direct-acting antiviral agents. The aim of the present study was to compile the current data on potential plant metabolites that can be investigated in COVID-19 drug discovery and development. This review represents a collection of plant secondary metabolites and their mode of action against SARS-CoV and SARS-CoV-2.

In silico and in vitro analyses of a novel FoxO1 agonist reducing Aβ levels via downregulation of BACE1

AIMS

FoxO1 is an important target in the treatment of Alzheimer's disease (AD). However, FoxO1-specific agonists and their effects on AD have not yet been reported. This study aimed to identify small molecules that upregulate the activity of FoxO1 to attenuate the symptoms of AD.

METHODS

FoxO1 agonists were identified by in silico screening and molecular dynamics simulation. Western blotting and reverse transcription-quantitative polymerase chain reaction assays were used to assess protein and gene expression levels of P21, BIM, and PPARγ downstream of FoxO1 in SH-SY5Y cells, respectively. Western blotting and enzyme-linked immunoassays were performed to explore the effect of FoxO1 agonists on APP metabolism.

RESULTS

N-(3-methylisothiazol-5-yl)-2-(2-oxobenzo[d]oxazol-3(2H)-yl) acetamide (compound D) had the highest affinity for FoxO1. Compound D activated FoxO1 and regulated the expression of its downstream target genes, P21, BIM, and PPARγ. In SH-SY5Y cells treated with compound D, BACE1 expression levels were downregulated, and the levels of Aβ1-40 and Aβ1-42 were also reduced.

CONCLUSIONS

We present a novel small-molecule FoxO1 agonist with good anti-AD effects. This study highlights a promising strategy for new drug discovery for AD.

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25μg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200 ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.

Virtual Screening, Molecular Docking, and Dynamic Simulations Revealed TGF-β1 Potential Inhibitors to Curtail Cervical Cancer Progression

Cervical cancer is one of the main causes of cancer death in women globally, and its epidemiology is similar to that of a low-infectious venereal illness. Many sexual partners and early age at first intercourse have been demonstrated to have a significant influence on risk. TGF-β1 is a multifunctional cytokine that is required for cervical carcinoma metastasis, tumor development, progression, and invasion. The TGF-β1 signaling system plays a paradoxical function in cancer formation, suppressing early-stage tumor growth while increasing tumor progression and metastasis. Importantly, TGF-β1 and TGF-β receptor 1 (TGF-βR1), two components of the TGF-β signaling system, are substantially expressed in a range of cancers, including breast cancer, colon cancer, gastric cancer, and hepatocellular carcinoma. The current study aims to investigate possible inhibitors targeting TGF-β1 using molecular docking and dynamic simulations. To target TGF-β1, we used anti-cancer drugs and small molecules. MVD was utilized for virtual screening, and the highest scoring compound was then subjected to MD simulations using Schrodinger software package v2017-1 (Maestro v11.1) to identify the most favorable lead interactions against TGF-β1. The Nilotinib compound has shown the least XP Gscore of -2.581 kcal/mol, 30ns MD simulations revealing that the Nilotinib- TGF-β1 complex possesses the lowest energy of -77784.917 kcal/mol. Multiple parameters, including Root Mean Square Deviation, Root Mean Square Fluctuation, and Intermolecular Interactions, were used to analyze the simulation trajectory. Based on the results; we conclude that the ligand nilotinib appears to be a promising prospective TGF-β1inhibitor for reducing TGF-β1 expression ad halting cervical cancer progression.

A. E. Albadri A, Patel H, Messaoudi S, Kadri A, M. Al-Hazmy S, Aouadi K. Synthesis, optical properties, DNA, β-cyclodextrin interaction, hydrogen isotope sensor and computational study of new enantiopure isoxazolidine derivative (ISoXD)

A novel isoxazolidine derivative (ISoXD) dye was successfully synthesized and comprehensively characterized. In this study, we conducted a thorough examination of its various properties, including optical characteristics, interactions with DNA and β-cyclodextrin (β-CD), molecular docking, molecular dynamic simulation, and density functional theory (DFT) calculations. Our investigation encompassed a systematic analysis of the absorption and emission spectra of ISoXD in diverse solvents. The observed variations in the spectroscopic data were attributed to the specific solvent's capacity to engage in hydrogen bonding interactions. Remarkably, the most pronounced intensities were observed in glycol, which can establish many hydrogen bonds with ISoXD. Furthermore, our study revealed a significant distinction in the fluorescence behavior of ISoXD when subjected to different solvents, particularly between CHCl3 and CDCl3. Moreover, we explored the fluorescence intensity of the ISoXD complex in the presence of various metals, both in ethanol and water. The ISoXD complex exhibited a substantial increase of fluorescence upon interaction with different metal ions. The utilization of DFT calculations allowed us to propose an intramolecular charge transfer (ICT) mechanism as a plausible explanation for this quenching phenomenon. The interaction of ISoXD with DNA and β-CD was studied using absorption spectra. The binding constant (K) and the standard Gibbs free energy change (ΔGo) for the interaction between DNA and β-CD with ISoXD were determined. In docking study, ISoXD exhibited significant docking scores (-6.511) and MM-GBSA binding free energies (-66.27 kcal/mol) within the PARP-1 binding cavity. Its binding pattern closely resembles to the co-crystal ligand veliparib, and during a 100ns MD simulation, ISoXD displayed strong stability and formed robust hydrogen bonds with key amino acids. These findings suggest ISoXD's potential as a PARP-1 inhibitor for further investigation in therapeutic development.

Computational simulations of bispecific T cell engagers by a multiscale model

The use of bispecific antibodies as T cell engagers can bypass the normal T cell receptor-major histocompatibility class interaction, redirect the cytotoxic activity of T cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when it is used to treat solid tumors. To avoid these adverse events, it is necessary to understand the fundamental mechanisms involved in the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and tumor-associated antigens (TAAs). The derived number of intercellular bonds formed between CD3 and TAAs was further transferred to the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights into how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody-binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a proof-of-concept study to help in the future design of new biological therapeutics.

Discovery of non-peptide GLP-1r natural agonists for enhancing coronary safety in type 2 diabetes patients

This study explores the computational discovery of non-peptide agonists targeting the Glucagon-Like Peptide-1 Receptor (GLP-1R) to enhance the safety of major coronary outcomes in individuals affected by Type 2 Diabetes. The objective is to identify novel compounds that can activate the GLP-1R pathway without the limitations associated with peptide agonists. Type 2 diabetes mellitus (T2DM) is associated with an increased risk of cardiovascular disease (CVD) and mortality, which is attributed to the accumulation of fat in organs, including the heart. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are frequently used to manage T2DM and could potentially offer cardiovascular benefits. Therefore, this study examines non-peptide agonists of GLP-1R to improve coronary safety in type 2 diabetes patients. After rigorous assessments, two standout candidates were identified, with natural compound 12 emerging as the most promising. This study represents a notable advancement in enhancing the management of coronary outcomes among individuals with type 2 diabetes. The computational methodology employed successfully pinpointed potential GLP-1R natural agonists, providing optimism for the development of safer and more effective therapeutic interventions. Although computational methodologies have provided crucial insights, realizing the full potential of these compounds requires extensive experimental investigations, crucial in advancing therapeutic strategies for this critical patient population.Communicated by Ramaswamy H. Sarma.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Novel pathogenic WHRN variant causing hearing loss in a moroccan family

OBJECTIVES

The most prevalent sensory disease in humans is deafness. A variety of genes have been linked to hearing loss, which can either be isolated (non-syndromic) or associated with lesions in other organs (syndromic). It has been discovered that WHRN variants are responsible for non-syndromic hearing loss and Usher syndrome type II.

METHODS AND RESULTS

Exome sequencing in a consanguineous Moroccan patient with severe hearing loss identified a single homozygous mutation c.619G > T; p.Ala207Ser in WHRN, encoding a cytoskeletal scaffold protein that binds membrane protein complexes to the cytoskeleton in ocular photoreceptors and ear hair cell stereocilia. Bioinformatics methods and molecular dynamic modeling were able to predict the pathogenic implications of this variation.

CONCLUSION

We used whole exome sequencing to find a homozygous WHRN gene variant in a Moroccan family. Numerous bioinformatics methods predict that this modification might result in a change in the WHRN protein's structure.

Integrating metabolomics, 16S rRNA sequencing, network pharmacology, and metorigin to explore the mechanism of Cinnamomi Cortex in treating chronic atrophic gastritis rats

BACKGROUND

Cinnamomi cortex called as Rougui (RG) in Chinese was a widely used food-medicine homology. RG has the potential to treat chronic atrophic gastritis (CAG), a disease with widespread impact in the Chinese population.

PURPOSE

This study aimed to explore its mechanism against CAG based on amalgamated strategies.

METHODS

Network pharmacology was used to predict the potential effective components and the core targets of RG against CAG based on the comprehensive chemical characterization using UHPLC-Q/TOF MS (ultra high performance liquid chromatogramphy-quadrupole/time-of-flight mass spectrometry). The CAG animals model were further used to validate its pharmacodynamics, of which gut microbiota of caecal contents were analyzed by integrating metabolomics, 16S rRNA sequencing, Metorigin metabolite traceability analysis and molecular docking to explore its action mechanism.

RESULTS

Network pharmacology firstly predicted the efficacy of RG was attributed to four effective components and seven targets. Metabolomics of caecal contents in CAG rats revealed primary bile acid biosynthesis was its targeted metabolic pathway associated with the metabolism of gut microbiota coupled with Metorigin traceability analysis. 16S rRNA sequencing showed that RG treated CAG by regulating the imbalance of gut microbiota. Molecular docking further confirmed that the effective components of RG could intervene with potential targets, metorigin analysis pathway, and key enzymes of gut microbiota metabolic pathways.

CONCLUSION

Our results proved that RG exerted favorable effect on CAG. The four active ingredients (quercetin, kaempferol, oleic acid, and (-)-epicatechin) of RG were the key to exert drug effect, which could targeted the core target of CAG, primary bile acid biosynthesis and intestinal flora metabolic pathways.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Multilayer precision-based screening of potential inhibitors targeting Mycobacterium tuberculosis acetate kinase using in silico approaches

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major threat to global health, with the emergence of multi-drug and extensively drug-resistant strains posing a serious challenge. Thereby, understanding the molecular basis of MTB virulence and disease pathogenesis is critical for developing effective therapeutic strategies. Targeting proteins involved in central metabolism has been recognized as a promising therapeutic approach to combat MTB. In this regard, the enzyme AckA of the acetate metabolic pathway which produces acetate from acetyl phosphate, is an important drug target for various pathogenic organisms. Therefore, this study aimed to identify potential AckA inhibitors through in silico methods, including molecular modeling, molecular dynamics simulation (MDS), and high-throughput virtual screening (HTVS) followed by ADMETox, MMGBSA, Density Functional Theory (DFT) calculations. HTVS of one million compounds from the ZINC database against AckA resulted in the top five hits (ZINC82048449, ZINC1219737510, ZINC1771921358, ZINC119699567, and ZINC1427100376) with better binding affinity and optimal binding free energy. MDS studies on complexes revealed that key residues, Asn195, Asp266, Phe267, Gly314, and Asn318 played a significant role in stable interactions of the top-ranked compounds to AckA. These outcomes provide insights into the optimal binding of the leads to inhibit the acetate pathway and aid in the rational design of novel therapeutic agents. Thus, the identified leads may act as promising compounds for targeting AckA and may serve as a potential therapeutic modality for treating TB. Our findings offer valuable insights into the inhibition of the acetate pathway, while also serving as a blueprint for rational drug design. The identified leads hold promise as compelling compounds for targeting AckA, thereby offering a potential therapeutic avenue for tackling TB. Thus, our study uncovers a pathway toward promising TB therapeutics by elucidating AckA inhibitors. By leveraging in silico methodologies, potent compounds that hold the potential to thwart AckA's role in MTB's acetate pathway have been unveiled. This breakthrough fosters optimism in the quest for novel and effective TB treatments, addressing a global health challenge with renewed vigor.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

Identification of novel small molecule inhibitors for solute carrier SGLT1; a computational exploration

Diabetes results in substantial disabilities, diminished quality of life, and mortality that imposes a huge economic burden on societies and governments worldwide. Despite the absence of specific oral therapies at present, there exists an urgent requirement to develop a novel drug for the treatment of diabetes mellitus. The membrane protein sodium glucose co-transporters (SGLT1) present a captivating therapeutic target for diabetes, given its pivotal role in facilitating glucose absorption in the small intestine, offering immense promise for potential therapeutic intervention. In this connection, the present study is aimed at identifying potential inhibitors of SGLT1 from a small molecule database, including compounds from both natural as well as synthetic origins. A comprehensive approach was employed, by integrating homology modeling, ligand-based pharmacophore modeling, virtual screening, and molecular docking simulation. The process resulted in the identification of 16 new compounds, featuring similar attributes as observed for the documented actives. In a systematic screening procedure, five potential virtual hits were selected for simulation studies followed by subsequent binding free energy calculations, providing deeper insight into the time-dependent behavior of protein-ligand complexes in a dynamic state. In conclusion, our findings demonstrated that the identified compounds, particularly compounds 81 and 91, exhibit enhanced stability and favorable binding affinities with the target protein, marking them promising candidates for further investigations.Communicated by Ramaswamy H. Sarma.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

Inhibition of cMYC-MAX transcription factors hetero-dimerization with structurally engineered omoMYC to downregulate oncogenic pathways in renal carcinoma

In the current study, we employed, structural informatics, and molecular simulation-based methods to engineer OmoMyc, a c-Myc dominant negative protein, to design novel mutants that could abrogate the c-MYC-MAX complex in Renal Carcinoma (RC). Among the total 472 mutations, only six mutations A61Q, Q64E, Q64K, N77R, Q64E-N77R, and Q64K-N77R were reported to increase the binding affinity and subjected to subsequent analysis such as protein-protein docking. The docking results revealed that the predicted mutants improve the functionality of the OmoMyc by not only increasing the binding affinity but also vdW and electrostatic energy in each complex that consequently increase the binding of the engineered OmoMyc by establishing extra hydrogen bonds, salt-bridges, and non-bonded contacts. Molecular simulation revealed a more stable behavior by the mutant complexes in contrast to the native OmoMyc however structural perturbations were reported in the regions, DBD (DNA-binding domain), loop region, and minor deviations at CTD (C terminal domain). Moreover, the hydrogen bonding and binding free energy results further validated the promising activity of our predicted mutants of OmoMyc. The results for TBE (total binding energy) revealed that the for each complex the TBE was calculated to be -87.88 ± 0.16 kcal/mol (WT OmoMyc-MAX), -91.89 ± 0.21 kcal/mol (A61Q OmoMyc-MAX), -91.55 ± 0.20 kcal/mol (Q64E OmoMyc-MAX), -95.17 ± 0.24 kcal/mol (Q64K OmoMyc-MAX), -96.49 ± 0.22 kcal/mol (N77R OmoMyc-MAX), -97.76 ± 0.22 kcal/mol (Q64E-N77R OmoMyc-MAX), and -95.31 ± 0.20 kcal/mol (Q64K-N77R OmoMyc-MAX) respectively. The results for TBE revealed promising results that allow the mutants to competitively inhibit the c-Myc-MAX complex more swiftly. Additionally, the internal motion and energy landscape were altered. These findings provide important insights into the potential of the mutants of OmoMyc as a therapeutic candidate for cancer treatment, particularly renal carcinoma, and could pave the way for the development of more effective clinical versions of OmoMyc.

Understanding the General Principles of T Cell Engagement by Multiscale Computational Simulations

The use of bispecific antibodies as T cell engagers can bypass the normal TCR-MHC interaction, redirect the cytotoxic activity of T-cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when they were used to treat solid tumors. In order to avoid these adverse events, it is necessary to understand the fundamental mechanisms during the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and TAA. The derived number of intercellular bonds formed between CD3 and TAA were further transferred into the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights of how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a prove-of-concept study to help the future design of new biological therapeutics.

SIGNIFICANCE

T-cell engagers are a class of anti-cancer drugs that can directly kill tumor cells by bringing T cells next to them. However, current treatments using T-cell engagers can cause serious side-effects. In order to reduce these effects, it is necessary to understand how T cells and tumor cells interact together through the connection of T-cell engagers. Unfortunately, this process is not well studied due to the limitations in current experimental techniques. We developed computational models on two different scales to simulate the physical process of T cell engagement. Our simulation results provide new insights into the general properties of T cell engagers. The new simulation methods can therefore serve as a useful tool to design novel antibodies for cancer immunotherapy.

Binding Mechanism of CD47 with SIRPα Variants and Its Antibody: Elucidated by Molecular Dynamics Simulations

The intricate complex system of the differentiation 47 (CD47) and the signal-regulatory protein alpha (SIRPα) cluster is a crucial target for cancer immunotherapy. Although the conformational state of the CD47-SIRPα complex has been revealed through crystallographic studies, further characterization is needed to fully understand the binding mechanism and to identify the hot spot residues involved. In this study, molecular dynamics (MD) simulations were carried out for the complexes of CD47 with two SIRPα variants (SIRPαv1, SIRPαv2) and the commercially available anti-CD47 monoclonal antibody (B6H12.2). The calculated binding free energy of CD47-B6H12.2 is lower than that of CD47-SIRPαv1 and CD47-SIRPαv2 in all the three simulations, indicating that CD47-B6H12.2 has a higher binding affinity than the other two complexes. Moreover, the dynamical cross-correlation matrix reveals that the CD47 protein shows more correlated motions when it binds to B6H12.2. Significant effects were observed in the energy and structural analyses of the residues (Glu35, Tyr37, Leu101, Thr102, Arg103) in the C strand and FG region of CD47 when it binds to the SIRPα variants. The critical residues (Leu30, Val33, Gln52, Lys53, Thr67, Arg69, Arg95, and Lys96) were identified in SIRPαv1 and SIRPαv2, which surround the distinctive groove regions formed by the B2C, C'D, DE, and FG loops. Moreover, the crucial groove structures of the SIRPα variants shape into obvious druggable sites. The C'D loops on the binding interfaces undergo notable dynamical changes throughout the simulation. For B6H12.2, the residues Tyr32_LC, His92_LC, Arg96_LC, Tyr32_HC, Thr52_HC, Ser53_HC, Ala101_HC, and Gly102_HC in its initial half of the light and heavy chains exhibit obvious energetic and structural impacts upon binding with CD47. The elucidation of the binding mechanism of SIRPαv1, SIRPαv2, and B6H12.2 with CD47 could provide novel perspectives for the development of inhibitors targeting CD47-SIRPα.

Investigating small molecule compounds targeting psoriasis based on cMAP database and molecular dynamics simulation

OBJECTIVE

Psoriasis (PSO) is a chronic inflammatory skin disease that severely affects the physical and mental health of patients. Drug resistance has been developed upon current drug treatments, and there is no specific therapy. The aim of this study was to screen promising novel drug candidates for PSO using molecular dynamics (MD) simulations.

METHODS

The data of PSO were downloaded from gene expression omnibus (GEO) database and subjected to variance analysis. Target proteins and small molecule compounds targeting PSO were predicted in the connective map (cMAP) database. Molecular docking, MD simulation, and trajectory analysis were conducted to predict the binding of target proteins to compounds.

RESULTS

1999 differentially expressed genes in PSO were obtained by differential analysis. Through cMAP database prediction, a low Score value of -45.69 for lymphocyte cell-specific protein-tyrosine kinase (LCK) was revealed, and aminogenistein was identified as the compound targeting LCK, and LCK was notably highly expressed in the PSO samples. The drugScore of the binding pocket P_0 was 0.814656, which was docked with aminogenistein. The results showed that there were more than one binding site between LCK and aminogenistein with binding energy less than -7.0 kJ/mol, and the docking was relatively stable. The results of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), Gyrate, number of hydrogen bonds and total free binding energy in MD simulations showed that the binding of aminogenistein to LCK was relatively solid.

CONCLUSION

Aminogenistein has good protein-ligand interaction and stability with LCK, a target of PSO, and is a novel drug candidate for PSO.

Effects of pH and NaCl on the Spatial Structure and Conformation of Myofibrillar Proteins and the Emulsion Gel System—Insights from Computational Molecular Dynamics on Myosin of Golden Pompano

In this study, the effects of pH and NaCl concentrations on the structure of golden pompano myosin and emulsion gel were analyzed using SEM in combination with molecular dynamics simulations (MDS). The microscopic morphology and spatial structure of myosin were investigated at different pH (3.0, 7.0, and 11.0) and NaCl concentrations (0.0, 0.2, 0.6, and 1.0 M), and their effects on the stability of emulsion gels were discussed. Our results show that pH had a greater effect on the microscopic morphology of myosin than NaCl. The MDS results show that under the condition of pH 7.0 and 0.6 M NaCl, the myosin expanded and experienced significant fluctuations in its amino acid residues. However, NaCl showed a greater effect on the number of hydrogen bonds than pH. Although changes in pH and NaCl concentrations only slightly altered the secondary structures in myosin, they, nevertheless, significantly influenced the protein spatial conformation. The stability of the emulsion gel was affected by pH changes but not NaCl concentrations, which only affect the rheology. The best elastic modulus G″ of the emulsion gel was obtained at pH 7.0 and 0.6 M NaCl. Based on the results, we conclude that pH changes have a greater influence than NaCl concentrations on the spatial structure and conformation of myosin, contributing to the instability of its emulsion gel state. The data from this study would serve as a valuable reference for emulsion gel rheology modification in future research.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Anti-HIV Potential of Beesioside I Derivatives as Maturation Inhibitors: Synthesis, 3D-QSAR, Molecular Docking and Molecular Dynamics Simulations

HIV-1 maturation is the final step in the retroviral lifecycle that is regulated by the proteolytic cleavage of the Gag precursor protein. As a first-in-class HIV-1 maturation inhibitor (MI), bevirimat blocks virion maturation by disrupting capsid-spacer peptide 1 (CA-SP1) cleavage, which acts as the target of MIs. Previous alterations of beesioside I (1) produced (20S,24S)-15ꞵ,16ꞵ-diacetoxy-18,24; 20,24-diepoxy-9,19-cyclolanostane-3ꞵ,25-diol 3-O-3′,3′-dimethylsuccinate (3, DSC), showing similar anti-HIV potency compared to bevirimat. To ascertain the binding modes of this derivative, further modification of compound 1 was conducted. Three-dimensional quantitative structure−activity relationship (3D-QSAR) analysis combined with docking simulations and molecular dynamics (MD) were conducted. Five new derivatives were synthesized, among which compound 3b showed significant activity against HIV-1NL4-3 with an EC50 value of 0.28 µM. The developed 3D-QSAR model resulted in great predictive ability with training set (r2 = 0.99, q2 = 0.55). Molecular docking studies were complementary to the 3D-QSAR analysis, showing that DSC was differently bound to CA-SP1 with higher affinity than that of bevirimat. MD studies revealed that the complex of the ligand and the protein was stable, with root mean square deviation (RMSD) values <2.5 Å. The above results provided valuable insights into the potential of DSC as a prototype to develop new antiviral agents.

Multi-source transfer learning with Graph Neural Network for excellent modelling the bioactivities of ligands targeting orphan G protein-coupled receptors

G protein-coupled receptors (GPCRs) have been the targets for more than 40% of the currently approved drugs. Although neural networks can effectively improve the accuracy of prediction with the biological activity, the result is undesirable in the limited orphan GPCRs (oGPCRs) datasets. To this end, we proposed Multi-source Transfer Learning with Graph Neural Network, called MSTL-GNN, to bridge this gap. Firstly, there are three ideal sources of data for transfer learning, oGPCRs, experimentally validated GPCRs, and invalidated GPCRs similar to the former one. Secondly, the SIMLEs format GPCRs convert to graphics, and they can be the input of Graph Neural Network (GNN) and ensemble learning for improving prediction accuracy. Finally, our experiments show that MSTL-GNN remarkably improves the prediction of GPCRs ligand activity value compared with previous studies. On average, the two evaluation indexes we adopted, R2 and Root-mean-square deviation (RMSE). Compared with the state-of-the-art work MSTL-GNN increased up to 67.13% and 17.22%, respectively. The effectiveness of MSTL-GNN in the field of GPCR Drug discovery with limited data also paves the way for other similar application scenarios.

Differences in the Elastomeric Behavior of Polyglycine-Rich Regions of Spidroin 1 and 2 Proteins

Two different polyglycine-rich fragments were selected as representatives of major ampullate gland spidroins (MaSp) 1 and 2 types, and their behavior in a water-saturated environment was simulated within the framework of molecular dynamics (MD). The selected fragments are found in the sequences of the proteins MaSp1a and MaSp2.2a of Argiope aurantia with respective lengths of 36 amino acids (MaSp1a) and 50 amino acids (MaSp2.2s). The simulation took the fully extended β-pleated conformation as reference, and MD was used to determine the equilibrium configuration in the absence of external forces. Subsequently, MD were employed to calculate the variation in the distance between the ends of the fragments when subjected to an increasing force. Both fragments show an elastomeric behavior that can be modeled as a freely jointed chain with links of comparable length, and a larger number of links in the spidroin 2 fragment. It is found, however, that the maximum recovery force recorded from the spidroin 2 peptide (F_max ≈ 400 pN) is found to be significantly larger than that of the spidroin 1 (F_max ≈ 250 pN). The increase in the recovery force of the spidroin 2 polyglycine-rich fragment may be correlated with the larger values observed in the strain at breaking of major ampullate silk fibers spun by Araneoidea species, which contain spidroin 2 proteins, compared to the material produced by spider species that lack these spidroins (RTA-clade).

Design, Synthesis, Docking, DFT, MD Simulation Studies of a New Nicotinamide-Based Derivative: In Vitro Anticancer and VEGFR-2 Inhibitory Effects

A nicotinamide-based derivative was designed as an antiproliferative VEGFR-2 inhibitor with the key pharmacophoric features needed to interact with the VEGFR-2 catalytic pocket. The ability of the designed congener ((E)-N-(4-(1-(2-(4-benzamidobenzoyl)hydrazono)ethyl)phenyl)nicotinamide), compound 10, to bind with the VEGFR-2 enzyme was demonstrated by molecular docking studies. Furthermore, six various MD simulations studies established the excellent binding of compound 10 with VEGFR-2 over 100 ns, exhibiting optimum dynamics. MM-GBSA confirmed the proper binding with a total exact binding energy of −38.36 Kcal/Mol. MM-GBSA studies also revealed the crucial amino acids in the binding through the free binding energy decomposition and declared the interactions variation of compound 10 inside VEGFR-2 via the Protein–Ligand Interaction Profiler (PLIP). Being new, its molecular structure was optimized by DFT. The DFT studies also confirmed the binding mode of compound 10 with the VEGFR-2. ADMET (in silico) profiling indicated the examined compound’s acceptable range of drug-likeness. The designed compound was synthesized through the condensation of N-(4-(hydrazinecarbonyl)phenyl)benzamide with N-(4-acetylphenyl)nicotinamide, where the carbonyl group has been replaced by an imine group. The in-vitro studies were consonant with the obtained in silico results as compound 10 prohibited VEGFR-2 with an IC₅₀ value of 51 nM. Compound 10 also showed antiproliferative effects against MCF-7 and HCT 116 cancer cell lines with IC₅₀ values of 8.25 and 6.48 μM, revealing magnificent selectivity indexes of 12.89 and 16.41, respectively.

Curing and Molecular Dynamics Simulation of MXene/Phenolic Epoxy Composites with Different Amine Curing Agent Systems

Herein, the curing kinetics and the glass transition temperature (T_g) of MXene/phenolic epoxy composites with two curing agents, i.e., 4,4-diaminodiphenyl sulfone (DDS) and dicyandiamine (DICY), are systematically investigated using experimental characterization, mathematical modeling and molecular dynamics simulations. The effect of MXene content on an epoxy resin/amine curing agent system is also studied. These results reveal that the MXene/epoxy composites with both curing agent systems conform to the SB(m,n) two-parameter autocatalytic model. The addition of MXene accelerated the curing of the epoxy composite and increased the T_g by about 20 K. In addition, molecular dynamics were used to simulate the T_g of the cross-linked MXene/epoxy composites and to analyze microstructural features such as the free volume fraction (FFV). The simulation results show that the introduction of MXene improves the T_g and FFV of the simulated system. This is because the introduction of MXene restricts the movement of the epoxy/curing agent system. The conclusions are in good agreement with the experimental results.

Computational Investigations on the Natural Small Molecule as an Inhibitor of Programmed Death Ligand 1 for Cancer Immunotherapy

Several therapeutic monoclonal antibodies approved by the FDA are available against the PD-1/PD-L1 (programmed death 1/programmed death ligand 1) immune checkpoint axis, which has been an unprecedented success in cancer treatment. However, existing therapeutics against PD-L1, including small molecule inhibitors, have certain drawbacks such as high cost and drug resistance that challenge the currently available anti-PD-L1 therapy. Therefore, this study presents the screening of 32,552 compounds from the Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening followed by binding free energy to refine the ideal conformation of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>−60 kcal/mol) for further computational investigation in comparison to co-crystallized ligand, i.e., JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and end-point binding free energy calculations, the selected natural compounds were marked for substantial stability with PD-L1 via intermolecular interactions (hydrogen and hydrophobic) with essential residues in comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitors in cancer immunotherapy.